Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction

TLDR

Vessel detection, tracking, state estimation, and trajectory prediction are critical for maritime safety, yet current vessel navigation and traffic monitoring systems lack advanced intelligent capabilities. This study proposes integrating intelligent features into vessel traffic monitoring systems to enhance their performance. The authors employ an artificial neural network to detect and track multiple vessels, and an extended Kalman filter to estimate states and predict trajectories for single vessels. Simulation results demonstrate that the proposed system successfully performs detection, tracking, state estimation, and trajectory prediction.

Abstract

Maneuvering vessel detection and tracking (VDT), incorporated with state estimation and trajectory prediction, are important tasks for vessel navigational systems (VNSs), as well as vessel traffic monitoring and information systems (VTMISs) to improve maritime safety and security in ocean navigation. Although conventional VNSs and VTMISs are equipped with maritime surveillance systems for the same purpose, intelligent capabilities for vessel detection, tracking, state estimation, and navigational trajectory prediction are underdeveloped. Therefore, the integration of intelligent features into VTMISs is proposed in this paper. The first part of this paper is focused on detecting and tracking of a multiple-vessel situation. An artificial neural network (ANN) is proposed as the mechanism for detecting and tracking multiple vessels. In the second part of this paper, vessel state estimation and navigational trajectory prediction of a single-vessel situation are considered. An extended Kalman filter (EKF) is proposed for the estimation of vessel states and further used for the prediction of vessel trajectories. Finally, the proposed VTMIS is simulated, and successful simulation results are presented in this paper.